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Google's imaging tech will advance robots and search

Google has detailed its latest image recognition capabilities, through which software can identify multiple objects in a room to a very accurate degree. The research has just been placed first in the classification and detection categories of the world's largest academic challenge in computer vision.

These developments have obvious benefits in areas such as robotics and self-driving cars -- both of which Google has invested heavily in. Mass-scale image recognition could also have a huge impact on search, however, as the algorithm could be used to more accurately comb through the reams of pictures on the web that already show up in Google Image Search or images within YouTube videos. In a blog post, Google adds that it could be deployed in "any place where it is useful to understand what is in an image as well as where things are".

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The categories of the ImageNet Large-Scale Visual Recognition Challenge that the team was successful in show that the algorithm was able not only to assign correct labels to an image, but to annotate a complex scene by accurately locating and identifying many objects within it. The Google team included two interns and called itself GoogLeNet after famous convolutional network LeNet, created by computer scientist Yann LeCun.

The core of Google's research involved a "radically redesigned" convolutional network that has improved depth and width, so has a wider field of vision. But it is also able to understand the structure of the items in an image.

It goes without saying that this is some seriously advanced computer science, but if you are keen to read more about how the GoogLeNet team developed it than head over to the Google Research blog post. The team entered the competition with an open submission, which means that it has shared all of the elements of its approach in the hope of fostering collaboration and accelerating progress.